Machine model predicting device of motor control device

ABSTRACT

In an electric motor control apparatus including an electric motor  4  for driving a load machine, a rotation detector  3  for detecting a rotating angle of the electric motor  4 , and a servo control device  2  for controlling the electric motor  4 , a calculating device  1  automatically calculates protruded shapes to be a resonance frequency and an anti-resonance frequency from frequency characteristics obtained from an operation command signal  8  and a rotation detector signal  9 , and furthermore, calculates errors of frequency characteristics calculated by frequency characteristic equations  20  for a 2-inertia model and a rigid body model from a frequency characteristic obtained by a measurement, and compares the minimum values of calculated values in the frequency characteristics of the 2-inertia model and the rigid body model and measured values respectively, thereby automatically modeling the characteristic of the machine.  
     Consequently, it is possible to provide a machine model estimating device of an electric motor control apparatus which can easily estimate a machine model without using an expensive measuring apparatus even if an operator has neither advanced expertise nor experiences, and furthermore, is inexpensive.

TECHNICAL FIELD

[0001] The present invention relates to a machine model estimatingdevice of an electric motor control apparatus which can faithfullyestimate a machine model to be easily utilized for a simulation and aservo regulation by automatically reading an anti-resonance frequency, aresonance frequency and an attenuation from a frequency characteristicmeasured value without using an expensive measuring apparatus even if anoperator has neither advanced expertise nor experiences, and isinexpensive.

BACKGROUND ART

[0002] Conventionally, an electric motor control apparatus to be used ina semiconductor manufacturing apparatus, a positioning apparatus such asa machine tool or an industrial robot is constituted as shown in FIG.15.

[0003]FIG. 15 is a view showing the whole structure of the electricmotor control apparatus according to the conventional art, anddescription will be given by taking a positioning apparatus as anexample.

[0004] In the drawing, 2 denotes a servo control device, 3 denotes arotation detector, 4 denotes an electric motor, 5 denotes a transmittingmechanism, 6 denotes a movable section, and 7 denotes a non-movablesection. In this case, the transmitting mechanism 5 and the movablesection 6 which constitute a load machine indicate a ball screw and atable respectively, and the non-movable section 7 indicates a base.Moreover, 8 denotes an operation command signal, 9 denotes a rotationdetector signal, and 10 denotes a control signal. Furthermore, 17denotes a signal generator and 18 denotes an FFT analyzer, and both ofthem grasp the frequency characteristic of the load machine and are usedfor devices required for the servo regulation of the control apparatus.

[0005] In such an electric motor control apparatus, first of all, thesignal generator 17 outputs the operation command signal 8 and theoperation command signal 8 is then sent to the servo control device 2.Next, the operation command signal 8 input to the servo control device 2is sent as the control signal 10 to the electric motor 4, and operatesthe movable section 6 through the transmitting mechanism 5 by therotating force of the electric motor 4. Thereafter, the rotationdetector 3 sends the rotation detector signal 9 of the electric motor 4to the FFT analyzer 18 through the servo control device 2. Subsequently,the FFT analyzer 18 carries out a fast Fourier calculation by using theoperation command signal 8 received from the signal generator 17 and therotation detector signal 9 received from the servo control device 2 andthen calculates a frequency characteristic, and decides thecharacteristic of the load machine from the result of the calculation.

[0006] In the conventional art, however, the expensive FFT analyzer 18is required for measuring the frequency characteristic of the loadmachine. Therefore, there is a problem in that the cost of equipment isincreased. In order to decide the frequency characteristic measured bythe FFT analyzer 18, moreover, an operator requires advanced expertiseand experiences for reading a resonance frequency, an anti-resonancefrequency and an attenuation. For this reason, there is a problem inthat time and labor are taken.

[0007] When the servo regulation of the electric motor control apparatusis to be carried out, therefore, there has been required an apparatuscapable of automatically reading an anti-resonance frequency, aresonance frequency and an attenuation from a frequency characteristicobtained by an actual measurement and modeling the characteristic of amachine which can be utilized for the simulation and the servoregulation of the control apparatus.

[0008] The invention has been made in order to solve the problems andhas an object to provide a machine model estimating device of anelectric motor control apparatus which can estimate a machine model tobe easily utilized for a simulation and a servo regulation byautomatically reading an anti-resonance frequency, a resonance frequencyand an attenuation from a frequency characteristic measured valuewithout using an expensive measuring apparatus even if an operator hasneither advanced expertise nor experiences, and is inexpensive.

DISCLOSURE OF THE INVENTION

[0009] In order to solve the problems, a first aspect of the inventionis directed to a machine model estimating device of an electric motorcontrol apparatus comprising an electric motor for driving a loadmachine, a rotation detector for detecting a rotating angle of theelectric motor, and a servo control device for controlling the electricmotor, comprising a calculating device for outputting an operationcommand signal for operating the electric motor to the servo controldevice, and frequency characteristic equations for a rigid body modeland an N-inertia model (N is an integer which is equal to or greaterthan 2) which are previously input to the calculating device, whereinthe calculating device includes a frequency characteristic measuringsection for measuring a frequency characteristic from the operationcommand signal and a signal of the rotation detector input from theservo control device to the calculating device, a frequencycharacteristic peak detecting section for automatically calculatingprotruded shapes to be a resonance frequency and an anti-resonancefrequency from a shape of the frequency characteristic measured by thefrequency characteristic measuring section, an attenuation estimationvalue analyzing section for estimating an attenuation from the resonancefrequency and the anti-resonance frequency which are detected by thefrequency characteristic peak detecting section, a frequencycharacteristic error calculating section for calculating errors of thefrequency characteristics calculated in the frequency characteristicequation for the N-inertia model and the frequency characteristicequation for the rigid body model from the frequency characteristicobtained by the measurement respectively, and a machine model decidingsection for comparing a minimum error of a calculated value of thefrequency characteristic of the N-inertia model which is obtained in thefrequency characteristic error calculating section and a measured valuewith a minimum error of a calculated value of the frequencycharacteristic of the rigid body model and a measured value anddeciding, as an actual model, either of the models which has a smallererror.

[0010] Moreover, a second aspect of the invention is directed to themachine model estimating device of an electric motor control apparatusaccording to the first aspect of the invention, wherein the frequencycharacteristic error calculating section carries out curve fitting ofthe frequency characteristic obtained from the operation command signaland the signal of the rotation detector to the frequency characteristicequation, thereby calculating an error of the calculated value of thefrequency characteristic and the measured value.

[0011] Furthermore, a third aspect of the invention is directed to amachine model estimating device of an electric motor control apparatuscomprising an electric motor for driving a load machine, a vibrationdetector for detecting a vibration of the load machine, and a servocontrol device for controlling the electric motor, comprising acalculating device for outputting an operation command signal foroperating the electric motor to the servo control device, and frequencycharacteristic equations of a rigid body model and an N-inertia model (Nis an integer which is equal to or greater than 2) which are previouslyinput to the calculating device, wherein the calculating device includesa frequency characteristic measuring section for measuring a frequencycharacteristic from the operation command signal and a signal of thevibration detector input from the servo control device to thecalculating device, a frequency characteristic peak detecting sectionfor automatically calculating protruded shapes to be a resonancefrequency and an anti-resonance frequency from a shape of the frequencycharacteristic measured by the frequency characteristic measuringsection, an attenuation estimation value analyzing section forestimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section, a frequency characteristic errorcalculating section for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation forthe N-inertia model and the frequency characteristic equation for therigid body model from the frequency characteristic obtained by themeasurement respectively, and a machine model deciding section forcomparing a minimum error of a calculated value of the frequencycharacteristic of the N-inertia model which is obtained in the frequencycharacteristic error calculating section and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.

[0012] In addition, a fourth aspect of the invention is directed to themachine model estimating device of an electric motor control apparatusaccording to the third aspect of the invention, wherein the frequencycharacteristic error calculating section carries out curve fitting ofthe frequency characteristic obtained from the operation command signaland the signal of the vibration detector to the frequency characteristicequation, thereby calculating an error of the calculated value of thefrequency characteristic and the measured value.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013]FIG. 1 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto a first embodiment of the invention. FIG. 2 is a block diagramshowing the structure of a calculating device according to the firstembodiment. FIG. 3 is a flowchart related to the measurement of afrequency characteristic in a procedure for the calculating operation ofthe calculating device according to the first embodiment. FIG. 4 is aflow chart related to an operation for deciding a machine model based onthe frequency characteristic value thus measured in the procedure forthe calculating operation of the calculating device according to thefirst embodiment. FIG. 5 is a view schematically showing a rigid bodymodel. FIG. 6 is a view schematically showing a 2-inertia model. FIG. 7is a chart showing an example of the frequency characteristic of therigid body model according to the first embodiment. FIG. 8 is a chartshowing an example of the frequency characteristic of the 2-inertiamodel according to the first embodiment. FIG. 9 is a chart showing anexample of a curve fitting result of a rigid body model type accordingto the first embodiment. FIG. 10 is a chart showing an example of acurve fitting result of a 2-inertia model type according to the firstembodiment. FIG. 11 is a chart showing an example of the curve fittingresult of the 2-inertia model type having a great error according to thefirst embodiment. FIG. 12 is a chart showing an example of an unfitnessto the curve fitness of the rigid body model type according to the firstembodiment. FIG. 13 is a chart showing an example of an unfitness to thecurve fitness of the 2-inertia model type according to the firstembodiment. FIG. 14 is a view showing the whole structure of an electricmotor control apparatus comprising a machine model estimating deviceaccording to a second embodiment of the invention. FIG. 15 is a viewshowing the whole structure of an electric motor control apparatusaccording to the conventional art.

BEST MODE FOR CARRYING OUT THE INVENTION

[0014] Embodiments of the invention will be described below withreference to the drawings.

[0015] [First Embodiment]

[0016]FIG. 1 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto a first embodiment of the invention, and FIG. 2 is a block diagramshowing the structure of a calculating device. The same components ofthe invention as those in the conventional art have the same referencenumerals and description thereof will be omitted, and furthermore, onlydifferences will be described.

[0017] In the drawing, 1 denotes a calculating device, 1A denotes afrequency characteristic measuring section, 1B denotes a frequencycharacteristic peak detecting section, 1C denotes an attenuationestimation value analyzing section, 1D denotes a frequencycharacteristic error calculating section, 1E denotes a machine modeldeciding section, 19 denotes an input device, 20 denotes a frequencycharacteristic equation, and 21 denotes an output device.

[0018] The invention is different from the conventional art as follows.

[0019] More specifically, there are provided the calculating device 1for outputting, to a servo control device 2, an operation command signal8 to operate an electric motor 4, and the frequency characteristicequations 20 for a rigid body model and a 2-inertia model which arepreviously input to the calculating device 1.

[0020] Moreover, the calculating device 1 includes the frequencycharacteristic measuring section 1A for measuring the frequencycharacteristic of a load machine from the operation command signal 8 anda signal 9 of a rotation detector 3 which is input from the servocontrol device 2 to the calculating device 1, the frequencycharacteristic peak detecting section 1B for automatically calculatingprotruded shapes to be a resonance frequency and an anti-resonancefrequency from a shape of the frequency characteristic measured by thefrequency characteristic measuring section 1A, the attenuationestimation value analyzing section 1C for estimating an attenuation fromthe resonance frequency and the anti-resonance frequency which aredetected by the frequency characteristic peak detecting section 1B, thefrequency characteristic error calculating section 1D for calculatingerrors of the frequency characteristics calculated in the frequencycharacteristic equation 20 for the 2-inertia model and the frequencycharacteristic equation 20 for the rigid body model from the frequencycharacteristic obtained by the measurement respectively, and the machinemodel deciding section 1E for comparing a minimum error of a calculatedvalue of the frequency characteristic of the 2-inertia model which isobtained in the frequency characteristic error calculating section 1Dand a measured value with a minimum error of a calculated value of thefrequency characteristic of the rigid body model and a measured valueand deciding, as an actual model, either of the models which has asmaller error.

[0021] Furthermore, the frequency characteristic error calculatingsection 1D carries out curve fitting of the frequency characteristicobtained from the operation command signal 8 and the signal 9 of therotation detector 3 to the frequency characteristic equation 20, therebycalculating an error of the calculated value of the frequencycharacteristic and the measured value.

[0022] Next, an operation will be described.

[0023]FIG. 3 is a flow chart related to the measurement of a frequencycharacteristic in a procedure for the calculating operation of thecalculating device according to the first embodiment, and FIG. 4 is aflow chart related to an operation for deciding a machine model based onthe frequency characteristic value thus measured in the procedure forthe calculating operation of the calculating device according to thefirst embodiment.

[0024] The procedure for the calculating operation of the calculatingdevice 1 is divided into steps ST1 to ST5 for measuring a frequencycharacteristic (FIG. 3) and steps ST6 to ST11 for comparing a modelbased on the frequency characteristic equation 20 with a measuredfrequency characteristic to model the characteristic of a machine (FIG.4).

[0025] The processings of the steps ST4 and ST5 correspond to thefrequency characteristic measuring section 1A of the calculating device1 shown in FIG. 2, and the processings of the steps ST6 and ST7correspond to the frequency characteristic peak detecting section 1B.Moreover, the processing of the step ST8 corresponds to the attenuationestimation value analyzing section 1C, the processings of the steps ST9and ST10 correspond to the frequency characteristic error calculatingsection 1D, and the processing of the step STl1 corresponds to themachine model deciding section 1E.

[0026] First of all, the measurement of the frequency characteristic inthe steps ST1 to ST5 will be described with reference to FIG. 3.

[0027] At the step ST1, first of all, the calculating device 1 createsthe operation command signal 8.

[0028] At the step ST2, next, the operation command signal 8 output fromthe calculating device 1 is transferred to the servo control device 2.Consequently, an equivalent control signal 10 to the operation commandsignal 8 is sent to the electric motor 4 and the electric motor 4 isoperated so that a movable section 6 is operated through a transmittingmechanism 5 and generates a vibration.

[0029] At the step ST3, then, the rotation detector 3 detects therotation detector signal 9 in the rotating operation of the electricmotor 4 and transfers the rotation detector signal 9 to the calculatingdevice 1 via the servo control device 2.

[0030] At the step ST4, thereafter, ah FFT calculation is carried outover the operation command signal 8 and the rotation detector signal 9by the calculating device 1, thereby performing a frequency analysis,for example.

[0031] At the step ST5, a frequency characteristic is calculated fromthe operation command signal 8 and the rotation detector signal 9 whichare subjected to the frequency analysis in the calculating device 1. Bythese processings, the frequency characteristic is completely measured.

[0032] Next, description will be given to the modeling of the machinecharacteristic at the steps ST6 to ST11.

[0033] Referring to the modeling of the machine characteristic, first ofall, a division into the rigid body model and the 2-inertia model can becarried out as shown in FIGS. 5 and 6, respectively.

[0034]FIG. 5 is a view schematically showing the rigid body model. Morespecifically, the electric motor 4, the transmitting mechanism 5 and themovable section 6 shown in FIG. 1 are caused to approximate to a simplerigid body load 11 (J1+J2). Moreover, FIG. 6 is a view schematicallyshowing the 2-inertia model. More specifically, the electric motor 4,the transmitting mechanism 5 and the movable section 6 shown in FIG. 1are caused to approximate to the 2-inertia model by two boxes having anelectric motor side load 12 (J1) and a load side load 13 (J2), and aspring 14 a (K: spring constant) and an attenuation 14 b (D: attenuationconstant) connecting the two boxes 12 and 13. The rigid body load 11 isequivalent to (J1+J2) to be the sum of the electric motor side load 12and the load side load 13.

[0035] In the rigid body model shown in FIG. 5, a model equation for afrequency characteristic Hr from the operation command signal 8 to therotation detector signal 9 is obtained as shown in Equation (1).$\begin{matrix}{H_{r} = \frac{1}{\left( {J_{1} + J_{2}} \right) \cdot s}} & {{Equation}\quad (1)}\end{matrix}$

[0036] In the 2-inertia model shown in FIG. 6, moreover, a modelequation for a frequency characteristic H_(f) from the operation commandsignal 8 to the rotation detector signal 9 is obtained as shown inEquation (2). $\begin{matrix}{H_{f} = {\frac{1}{J_{1} \cdot s} \cdot \frac{{S2} + {\frac{D}{J2} \cdot s} + \frac{K}{J2}}{s^{2} + {\left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right) \cdot D \cdot s} + {\left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right) \cdot K}}}} & {{Equation}\quad (2)}\end{matrix}$

[0037] In order to approximate to the rigid body model, it is sufficientthat the sum (J1+J2) of the rigid body load 11 or the electric motorside load 12 and the load side load 13 is clear.

[0038] In order to approximate to the 2-inertia model, moreover, it issufficient that the electric motor side load 12 (J1) and the load sideload 13 (J2), and the spring constant K and the attenuation constant Dare clear.

[0039]FIG. 7 is a chart showing an example of the frequencycharacteristic of the rigid body model according to the first embodimentand FIG. 8 is a chart showing an example of the frequency characteristicof the 2-inertia model according to the first embodiment, and both ofthem are charts showing a calculation using model equations.

[0040] The Equation (1) for the rigid body model represents a gaincharacteristic which is smooth, rightward and downward as shown in FIG.7, while the Equation (2) for the 2-inertia model represents a gaincharacteristic having a protruded shape, that is, a mountain and avalley as shown in FIG. 8. The valley and mountain sides shown in FIG. 8are referred to as an anti-resonance and a resonance respectively, andan anti-resonance frequency F_(L) and a resonance frequency F_(H) canapproximate to Equations (3) and (4) from the Equation (2) respectively.$\begin{matrix}{f_{L} = {\frac{1}{2\pi} \cdot \sqrt{\frac{K}{J_{2}}}}} & {{Equation}\quad (3)}\end{matrix}$

[0041] In this case, the rightward and downward inclination of a lowfrequency region in FIG. 8 can approximate to the Equation (1).

[0042] For this reason, the sum (J1+J2) of the anti-resonance frequencyFL, the resonance frequency F_(H), the electric motor side load 12 andthe load side load 13 is clear and an approximation to the 2-inertiamodel can be carried out.

[0043] Moreover, the sum (J1+J2) of the electric motor side load 12 andthe load side load 13 can be calculated by the dimension and physicalcharacteristic of the load machine. If (J1+J2) is previously calculated,furthermore, it can be input in the input device 19 connected to thecalculating device 1 (described in INERTIA IDENTIFYING METHOD ANDAUTOTUNING: VOL. 62, NO. 4, Technical Report “YASKAWA ELECTRICCORPORATION”, for example).

[0044] From the foregoing, it is possible to compare the measuredfrequency characteristic with the Equations (1) and (2), therebydeciding whether either the rigid body model or the 2-inertia model issuitable.

[0045]FIG. 9 is a chart showing an example of the curve fitting resultof the rigid body model type according to the first embodiment and FIG.10 is a chart showing an example of the curve fitting result of the2-inertia model type according to the first embodiment. In the drawing,a solid line indicates a measured value and a broken line indicates acurve fitting result.

[0046] Since a frequency characteristic changed smoothly from a left andupper portion toward a right and lower portion represents a measurementresult in FIG. 9, curve fitting can be carried out with a small errorfrom the graph of the Equation (1). On the other hand, since a frequencycharacteristic having a plurality of valleys and mountains represents ameasurement result in FIG. 10, the curve fitting can be carried out witha small error from the graph of the Equation (2).

[0047] A processing of modeling a machine characteristic will beexecuted below by using the steps ST6 to STl1 in the flow chart of FIG.3.

[0048] At the step ST6, first of all, a peak on a mountain side and apeak on a valley side are calculated from the frequency characteristicmeasured at the step ST5. For a method of calculating the peak, it ispreferable to use a complex spectrum interpolating method and asmoothing differentiation method which are well-known.

[0049] If the peak on the mountain side and the peak on the valley sidecannot be detected at the step ST7, next, the model can be decided to bea rigid body and the parameter of the rigid body can be determined byonly a load inertia to be the sum (J1+J2) of the electric motor sideload 12 and the load side load 13. If the peak can be detected at thestep ST7, the processing proceeds to the step ST8 in which anattenuation can be estimated based on a well-known attenuationestimating method by using the frequency of the peak thus detected.

[0050] If the sum (J1+J2) of the electric motor side load 12 and theload side load 13 is undecided, moreover, the load inertia (J1+J2) maybe calculated, by a least square method using the Equation (1), from thelow frequency region of the measured frequency characteristic which islower than the peak on the valley side.

[0051] If a plurality of peaks can be detected, furthermore, a pluralityof combinations to be a pair of the peak on the mountain side and thepeak on the valley side is set and created.

[0052] At the step ST9, then, the combination to be a peak pair which isset temporarily is subjected to curve fitting by the frequencycharacteristic equation 20 for the 2-inertia model input previously tothe calculating device 1, that is, the Equation (2), thereby calculatingan error of the Equation (2) and the measured value.

[0053] Errors of results obtained by the curve fitting and the measuredfrequency characteristics are calculated by using the combinations ofthe peaks, respectively.

[0054] Since the load inertia (J1+J2), a pair of the peak on themountain side and the peak on the valley side, that is, a resonance andan anti-resonance frequency, and an attenuation are clear, it ispossible to carry out the curve fitting by substituting each value forthe Equation (2) to be one of the frequency characteristic equations 20input to the calculating device 1.

[0055] Any of the combinations which has a small error from the resultobtained by the curve fitting makes a set of the peak on the mountainside and the peak on the valley side which is optimum for the 2-inertiamodel.

[0056]FIG. 11 is a chart showing an example of the curve fitting resultof the 2-inertia model type having a great error according to the firstembodiment. In the drawing, a solid line indicates a measured value anda broken line indicates a curve fitting result.

[0057] For example, a set of the resonance and the anti-resonance shownin FIG. 10 has a small error, while a set of the resonance and theanti-resonance shown in FIG. 11 has a great error. Therefore, it isclear that the set of FIG. 10 is optimum for the resonance and theanti-resonance in the 2-inertia model.

[0058] At the step ST10, also in the case in which the peak is detected,an error of the result obtained by the curve fitting to the rigid bodymodel and the measured frequency characteristic is calculated by theEquation (1) to be one of the frequency characteristic equations 20input to the calculating device 1.

[0059] At the step ST11, next, the minimum error of the 2-inertia modelis compared with the error of the rigid body model. If the error of therigid body model is small, a decision of the rigid body model can bemade. If the error of the 2-inertia model is small, a decision of the2-inertia model can be made. In other words, it is possible to comparethe error of the Equation (2) and the measured frequency characteristicwith the error of the Equation (1) and the measured frequencycharacteristic, thereby deciding which error is smaller and which modelis optimum in the modeling for the Equation (1) and the Equation (2).

[0060]FIG. 12 is a chart showing an example of an unfitness to the curvefitting of the rigid body model type according to the first embodimentand FIG. 13 is a chart showing an example of an unfitness to the curvefitting of the 2-inertia model type according to the first embodiment.In the drawing, a solid line indicates a measured value and a brokenline indicates a curve fitting result.

[0061] While the measured value of the frequency characteristic in FIG.12 has a plurality of valleys and mountains, for example, the curvefitting is carried out in the Equation (1) for the rigid body model sothat an error is great. As shown in FIG. 10, however, the error is smallin the Equation (2) for the 2-inertia model. Therefore, a decision ofthe 2-inertia model can be made.

[0062] While the frequency characteristic changed smoothly from a leftand upper portion toward a right and lower portion represents ameasurement result, moreover, the curve fitting is carried out in theEquation (2) for the 2-inertia model so that the error is great. Asshown in FIG. 9, however, the error is small in the Equation (1) for therigid body model. Therefore, a decision of the rigid body model can bemade.

[0063] When the decision of the model is completed, the result can beoutput to the output device 21 connected to the calculating device 1 andcan be utilized for a simulation and the regulation of the electricmotor control apparatus.

[0064] Accordingly, the first embodiment is characterized by theelectric motor control apparatus comprising the electric motor 4 fordriving a load machine, the rotation detector 3 for detecting therotating angle of the electric motor 4, and the servo control device 2for controlling the electric motor 4, comprising the calculating device1 for outputting the operation command signal 8 for operating theelectric motor 4 to the servo control device 2, and the frequencycharacteristic equations 20 of the rigid body model and the 2-inertiamodel which are previously input to the calculating device 1. Moreover,the calculating device-1 includes the frequency characteristic measuringsection 1A for measuring the frequency characteristic of the loadmachine from the operation command signal 8 and the signal 9 of therotation detector 3 input from the servo control device 2 to thecalculating device 1, the frequency characteristic peak detectingsection 1B for automatically calculating protruded shapes to be aresonance frequency and an anti-resonance frequency from a shape of thefrequency characteristic measured by the frequency characteristicmeasuring section 1A, the attenuation estimation value analyzing section1C for estimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section 1B, the frequency characteristicerror calculating section 1D for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation 20for the 2-inertia model and the frequency characteristic equation 20 forthe rigid body model from the frequency characteristic obtained by themeasurement respectively, and the machine model deciding section 1E forcomparing a minimum error of a calculated value of the frequencycharacteristic of the 2-inertia model which is obtained in the frequencycharacteristic error calculating section 1D and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error. Furthermore, thefrequency characteristic error calculating section 1D carries out curvefitting of the frequency characteristic obtained from the operationcommand signal 8 and the signal 9 of the rotation detector 3 to thefrequency characteristic equation 20, thereby calculating an error ofthe calculated value of the frequency characteristic and the measuredvalue. Consequently, it is possible to provide a machine modelestimating device of an electric motor control apparatus which canfaithfully estimate a machine model to be easily utilized for asimulation and a servo regulation by automatically reading ananti-resonance frequency, a resonance frequency and an attenuation froma frequency characteristic measured value without using an expensivemeasuring apparatus even if an operator has neither advanced expertisenor experiences, and furthermore, is inexpensive.

[0065] [Second Embodiment]

[0066] A second embodiment of the invention will be described withreference to the drawings.

[0067]FIG. 14 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto the second embodiment of the invention.

[0068] In the drawing, 15 denotes a vibration detector for detecting theoperation state of a load machine as a vibration displacement or avibration acceleration, and 16 denotes a vibration detector signal ofthe load machine.

[0069] The second embodiment uses the vibration detector 15 in the loadmachine in place of the rotation detector described in the firstembodiment, and can be executed in the same manner as the firstembodiment.

[0070] A frequency characteristic Hr from an operation command signal 8of a rigid body model to the detector 15 is equal to that of theEquation (1). Moreover, a frequency characteristic H′_(F) from theoperation command signal 8 of a 2-inertia model to the vibrationdetector 15 of a load side load 13 is obtained from Equation (5).$\begin{matrix}{f_{H} = {\frac{1}{2\pi} \cdot \sqrt{K \cdot \left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right)}}} & {{Equation}\quad (4)}\end{matrix}$

[0071] Thus, the second embodiment is executed by using the vibrationdetector 15 in the load machine in place of the rotation detectoraccording to the first embodiment. In the same manner as in the firstembodiment, consequently, a peak on the mountain side of the measuredfrequency characteristic is estimated, an attenuation is estimated, aload inertia is estimated, a resonance is temporarily determined, andthe resonance is compared with the frequency characteristic of the modelwith a change and a resonance having a small error is obtained, andfurthermore, the frequency characteristics of the rigid body model andthe 2-inertia model are compared with each other, the model having asmaller error is distinguished, and the measured frequencycharacteristic is subjected to curve fitting. Thus, modeling can befaithfully executed.

[0072] While the two machine models, that is, the rigid body model andthe 2-inertia model are used in the embodiment, another model such as a3-inertia model may be used or the type of the model to be distinguishedmay be increased.

[0073] While only the model and the error of the measured frequencycharacteristic are set to be evaluation criteria in the embodiment,moreover, the value of the gain of a resonance frequency, the widths ofthe gains of an anti-resonance frequency and the resonance frequency,and a frequency may be added to the evaluation criteria.

INDUSTRIAL APPLICABILITY

[0074] As described above, the machine model estimating device of theelectric motor control apparatus according to the invention is usefulfor the servo regulation of an electric motor control apparatus to beused in a semiconductor manufacturing apparatus, a positioning apparatussuch as a machine tool or an industrial robot, for example.

1. A machine model estimating device of an electric motor controlapparatus comprising an electric motor for driving a load machine, arotation detector for detecting a rotating angle of the electric motor,and a servo control device for controlling the electric motor,comprising a calculating device for outputting an operation commandsignal for operating the electric motor to the servo control device, andfrequency characteristic equations for a rigid body model and anN-inertia model (N is an integer which is equal to or greater than 2)which are previously input to the calculating device, wherein thecalculating device includes a frequency characteristic measuring sectionfor measuring a frequency characteristic from the operation commandsignal and a signal of the rotation detector input from the servocontrol device to the calculating device, a frequency characteristicpeak detecting section for automatically calculating protruded shapes tobe a resonance frequency and an anti-resonance frequency from a shape ofthe frequency characteristic measured by the frequency characteristicmeasuring section, an attenuation estimation value analyzing section forestimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section, a frequency characteristic errorcalculating section for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation forthe N-inertia model and the frequency characteristic equation for therigid body model from the frequency characteristic obtained by themeasurement respectively, and a machine model deciding section forcomparing a minimum error of a calculated value of the frequencycharacteristic of the N-inertia model which is obtained in the frequencycharacteristic error calculating section and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.
 2. The machinemodel estimating device of an electric motor control apparatus accordingto claim 1, wherein the frequency characteristic error calculatingsection carries out curve fitting of the frequency characteristicobtained from the operation command signal and the signal of therotation detector to the frequency characteristic equation, therebycalculating an error of the calculated value of the frequencycharacteristic and the measured value.
 3. A machine model estimatingdevice of an electric motor control apparatus comprising an electricmotor for driving a load machine, a vibration detector for detecting avibration of the load machine, and a servo control device forcontrolling the electric motor, comprising a calculating device foroutputting an operation command signal for operating the electric motorto the servo control device, and frequency characteristic equations of arigid body model and an N-inertia model (N is an integer which is equalto or greater than 2) which are previously input to the calculatingdevice, wherein the calculating device includes a frequencycharacteristic measuring section for measuring a frequencycharacteristic from the operation command signal and a signal of thevibration detector input from the servo control device to thecalculating device, a frequency characteristic peak detecting sectionfor automatically calculating protruded shapes to be a resonancefrequency and an anti-resonance frequency from a shape of the frequencycharacteristic measured by the frequency characteristic measuringsection, an attenuation estimation value analyzing section forestimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section, a frequency characteristic errorcalculating section for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation forthe N-inertia model and the frequency characteristic equation for therigid body model from the frequency characteristic obtained by themeasurement respectively, and a machine model deciding section forcomparing a minimum error of a calculated value of the frequencycharacteristic of the N-inertia model which is obtained in the frequencycharacteristic error calculating section and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.
 4. The machinemodel estimating device of an electric motor control apparatus accordingto claim 3, wherein the frequency characteristic error calculatingsection carries out curve fitting of the frequency characteristicobtained from the operation command signal and the signal of thevibration detector to the frequency characteristic equation, therebycalculating an error of the calculated value of the frequencycharacteristic and the measured value.